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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)最新文献

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Efficient Mining of Closed Induced Ordered Subtrees in Tree-structured Databases 树结构数据库中闭合诱导有序子树的高效挖掘
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.67
Tomonobu Ozaki, T. Ohkawa
{"title":"Efficient Mining of Closed Induced Ordered Subtrees in Tree-structured Databases","authors":"Tomonobu Ozaki, T. Ohkawa","doi":"10.1109/ICDMW.2006.67","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.67","url":null,"abstract":"In this paper, two new closed ordered subtree miners are given which are based on the breadth-first and depth-first/breadth-first enumeration strategy, respectively. Through the experiments with synthesized and real world data, we discuss the effects of the difference of the search strategies in mining closed induced ordered subtrees","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116909768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A Study on Reliability in Graph Discovery 图发现的可靠性研究
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.23
H. Dai
{"title":"A Study on Reliability in Graph Discovery","authors":"H. Dai","doi":"10.1109/ICDMW.2006.23","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.23","url":null,"abstract":"A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this paper the authors provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124763924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images MACLAW与几种属性选择方法在高光谱图像分类中的比较
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.47
A. Blansché, A. Wania, P. Gançarski
{"title":"Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images","authors":"A. Blansché, A. Wania, P. Gançarski","doi":"10.1109/ICDMW.2006.47","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.47","url":null,"abstract":"MACLAW is a clustering algorithm with local attribute weighting performed through cooperative coevolution. In this paper, we will compare the attributes weights obtained by MACLAW with several relevance indices for band selection on DAIS remotely sensed image which registers spectral object information in 79 bands of at least 2 nm. MACLAW capacities are also assessed by comparing its results to a supervised classification method for feature extraction proposed by the software ENVI (RSI Inc.). The MACLAW results are satisfying. Classification results are similar to the results of the supervised method. Supervised classification results are slightly improved using only a feature subset identified by MACLAW","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120875670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Decision Tree Construction from Multidimensional Structured Data 基于多维结构化数据的决策树构建
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.57
Tomoki Watanuma, Tomonobu Ozaki, T. Ohkawa
{"title":"Decision Tree Construction from Multidimensional Structured Data","authors":"Tomoki Watanuma, Tomonobu Ozaki, T. Ohkawa","doi":"10.1109/ICDMW.2006.57","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.57","url":null,"abstract":"Since most structured data mining techniques specialize in mining from single structured data, it cannot handle more realistic data which consist of different and plural kinds of structured data. To cope with this problem, we propose an algorithm for constructing decision trees from multidimensional structured data by introducing the techniques for mining correlated and closed patterns with effective pruning capabilities into the traditional TDIDT approach. The results of the experiments with real world data show the effectiveness of the proposed algorithm","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis ARX模型订单搜索方法及其在销售动态分析中的应用
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.10
Kenta Fukata, T. Washio, H. Motoda
{"title":"A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis","authors":"Kenta Fukata, T. Washio, H. Motoda","doi":"10.1109/ICDMW.2006.10","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.10","url":null,"abstract":"An auto-regressive exogenous input (ARK) model has been widely used in engineering fields to model dynamic response of a system to exogenous factors. A difficulty in this modeling is the determination of an appropriate model complexity, i.e., orders, for given data. In this paper, we develop a new and practical approach to determine the appropriate orders. Moreover, we apply the developed technique to a real marketing data, and analyze dynamic response character of sales amount to advertisement and sales promotion. In marketing study, static response of sales to some exogenous factors such as advertisement and sales promotion have been analyzed. However, if we can model dynamic response of sales to exogenous factors, more precise strategies of the sales to reduce the risk of the item stock management and increase the associated profit can be designed","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"126 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129797313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Proposal of Medical KDD Support User Interface Utilizing Rule Interestingness Measures 基于规则兴趣度度量的医疗KDD支持用户界面的建议
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.137
M. Ohsaki, H. Abe, S. Tsumoto, H. Yokoi, Takahira Yamaguchi
{"title":"Proposal of Medical KDD Support User Interface Utilizing Rule Interestingness Measures","authors":"M. Ohsaki, H. Abe, S. Tsumoto, H. Yokoi, Takahira Yamaguchi","doi":"10.1109/ICDMW.2006.137","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.137","url":null,"abstract":"This paper discusses the utilization of rule interestingness measures in medical KDD. The authors selected various interestingness measures and conducted experiments using clinical datasets to examine how they can estimate real human interest. The results indicate that some of them have a stable, reasonable estimation performance and the combinational use of interestingness measures will contribute to medical KDD. We then developed a prototype of medical KDD support user interface based on the experimental outcomes. We conducted a case study in which a medical expert tried to discover medical knowledge with the prototype. Some interesting rules were actually obtained and that indicates the potential of the user interface","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130901757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates 一种基于增量更新的数据流封闭频繁项集维护新算法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.15
Hua-Fu Li, Chin-Chuan Ho, Fang-Fei Kuo, Suh-Yin Lee
{"title":"A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates","authors":"Hua-Fu Li, Chin-Chuan Ho, Fang-Fei Kuo, Suh-Yin Lee","doi":"10.1109/ICDMW.2006.15","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.15","url":null,"abstract":"Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"334-335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130915722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 23
Context-Inclusive Approach to Speed-up Function Evaluation for Statistical Queries : An Extended Abstract 统计查询加速函数求值的上下文包容方法:扩展摘要
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.52
Vijay Gandhi, James M. Kang, S. Shekhar, J. Ju, E. Kolaczyk, S. Gopal
{"title":"Context-Inclusive Approach to Speed-up Function Evaluation for Statistical Queries : An Extended Abstract","authors":"Vijay Gandhi, James M. Kang, S. Shekhar, J. Ju, E. Kolaczyk, S. Gopal","doi":"10.1109/ICDMW.2006.52","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.52","url":null,"abstract":"Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality estimation function. This problem is common in important applications like land-use classification at multiple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the quality estimation function for each candidate model. A proposed method of multiscale, multigranular classification has high computational overhead of function evaluation for various candidate models independently before comparison. In contrast, we propose a context-inclusive approach that controls the computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. Experimental results using land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly while providing comparable classification accuracy","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Segmentation of Time Series by the Clustering and Genetic Algorithms 基于聚类和遗传算法的时间序列分割
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.145
V. Tseng, Chun-Hao Chen, Chien-Hsiang Chen, T. Hong
{"title":"Segmentation of Time Series by the Clustering and Genetic Algorithms","authors":"V. Tseng, Chun-Hao Chen, Chien-Hsiang Chen, T. Hong","doi":"10.1109/ICDMW.2006.145","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.145","url":null,"abstract":"This paper proposes a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. The genetic algorithm is used to find the segmentation points for deriving appropriate patterns. In the fitness evaluation, the proposed algorithm first divides subsequences in a chromosome into k clusters by using the k-means clustering approach. The Euclidean distance is then used to calculate the distance of each subsequence and evaluate a chromosome. The discrete wavelet transformation is also used to adjust the length of the subsequences for comparing their similarity since their length may be different. Experimental results show that the proposed approach can get good effects in finding appropriate segmentation patterns in time series","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124827973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
A Maximum Likelihood Approach to Noise Estimation for Intensity Measurements in Biology 生物学中强度测量噪声估计的最大似然方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.9
F. Klawonn, C. Hundertmark, L. Jänsch
{"title":"A Maximum Likelihood Approach to Noise Estimation for Intensity Measurements in Biology","authors":"F. Klawonn, C. Hundertmark, L. Jänsch","doi":"10.1109/ICDMW.2006.9","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.9","url":null,"abstract":"Often, measurement of biological components generates results that are corrupted by noise. Noise can be caused by various factors like the detectors themselves, sample properties or also the process of data processing and appears independently from the applied technology. When measuring two identical samples it can be observed that similar signal intensities may have inherent but varying levels of noise and that the ratio of noise decreases with increasing signal intensities. In this paper a statistical approach is introduced to estimate the noise inherent in the measured data. Based on this estimation, it is possible to provide information about the reliability of a measured signal and whether the difference between intensities is mainly caused by noise or by biological relevant cellular alterations","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123123821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
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